No methods currently exist for multi-agent, distributed, privacy-preserving data stream mining system for characterizing vehicle, driver, and fleet monitoring. Existing monitoring systems work by downloading the data over wireless networks and then applying relatively simple linear threshold-based techniques for detecting unusual patterns.
Using data mining techniques for vehicle condition monitoring is a known art. Onboard driver performance measurement by mounting several sensors is also known. Such known systems, however, are directed primarily to performing vehicle diagnostics, assessing vehicle performance, or using sensors onboard to store the data on onboard systems and connecting the vehicle onboard computer to a remote computer for transmitting the data and visualizing it. There is no software that runs onboard a vehicle on a PDA or an embedded device and that uses lightweight data stream management and mining techniques for detecting driver's signature and continuously monitors as does the subject vehicle driver signature detection system.
For instance, U.S. Pat. No. 5,499,182 is directed to multiple vehicle component sensors mounted to a host vehicle measure vehicle component parameters indicative of a host vehicle's driver performance. A microprocessor module detachably coupled to the vehicle mounting unit affixed to and uniquely designated for a given host vehicle poles each vehicle sensor of that host vehicle to read, process, and store the vehicle operation data generated thereby. A playback mounting unit at a remote computer connects the remote computer to the host vehicle's microprocessor module in order to establish digital communication whereby the vehicle operation data and the analysis results processed therein are retrieved and displayed for a user. In addition, the driver integrity-checking module is based on some pre-determined values of the parameters and is done remotely after the data is played back on the remote computer. Also, the vehicle needs to be mounted by a multiple number of sensors as opposed to using the standard OBDII data bus for getting the vehicle data in the subject vehicle driver performance system.
U.S. Pat. No. 5,207,095 is directed to an onboard vehicle computer system for use in evaluating an operator's braking technique that employs a plurality of vehicle-mounted sensors. The onboard computer in that system periodically receives and stores the parametric values associated with vehicle braking sensed by the sensors. The data thus generated by that computer is then available to be read later by an instructor who compares the recorded parametric values to formulate further instructive steps. That system does not perform any lightweight and sophisticated onboard data mining techniques on the data. Any evaluations to be made in light of the raw data are left for the user to make by themselves. Furthermore, as the vehicle sensor monitoring system there is intended specifically as an instructional tool, monitoring is performed only during those discrete time intervals related to an instructional session.
U.S. Pat. No. 6,609,051 is directed to a vehicle condition-monitoring system that employs machine learning and data mining technologies on data acquired from a plurality of vehicles in order to create models. Frequent acquisition of vehicle sensor and diagnostic data enables comparison with the created models to provide continuing analysis of the vehicle for repair, maintenance and diagnostics. The on-board diagnostic systems process sensor readings and diagnostic information of the vehicle Embedded Control System in order to detect defaults. The maintenance systems on-board the vehicle continuously process sensor readings to determine the condition of the vehicle systems, parts and lubricants (e.g., brake pad wear, battery quality, and oil quality). Off-board diagnostic systems acquire vehicle diagnostics and sensor data or control on-board diagnostics and testing functions. The system uses OEM proprietary or standardized interfaces, for example, OBD to connect to the vehicle. Physical connections link the vehicle and the workshop test equipment, with short-range wireless communication systems eventually replacing cable connections.
U.S. Pat. No. 6,330,499 directs itself to a vehicle diagnostic and health monitoring system that includes a client computer device within the vehicle, coupled to the vehicle's monitoring systems, for data management, remote session management and user interaction, a communication system, coupled to the client computer device, for providing remote communication of data including data derived from internal monitoring systems of the vehicle, and a remote service center including a vehicle data store, a server computer, a diagnostic engine, and a communicator for communicating the results of analysis of vehicle information to the client computer device via the communication system.
U.S. Pat. No. 5,034,894 directs itself to a self-diagnosis computer system onboard a motor vehicle wherein a plurality of detectors are mounted on that vehicle's engine to detect any aberrant operating conditions. Although the computer system there performs continual monitoring while the vehicle is in operation, no provision is made for the assessment of driver performance based on any sensed parameters.
Similarly, U.S. Pat. No. 5,074,144 is directed to an onboard vehicle computer system for monitoring vehicle performance. Various transducers for continually monitoring various vehicle parameters are employed in that system; however, comprehensive means for analyzing the measured vehicle parameters to characterize or assess driver performance, per se, are not provided.
Prior state-of-the-art is based on linear threshold-based techniques that allow simple tasks such as detection of a feature value crossing a limit set a priori. Moreover, these techniques are applied after the data is uploaded to a remote desktop computer from the vehicle. For example, these techniques may check whether the driver crossed a specified speed limit. Unfortunately, these techniques are not capable of detecting linear and nonlinear complex driving patterns and they require an expensive process of transferring data to a remote monitoring station at a regular basis over the wireless network.
Needs exist for improved systems using mobile and distributed data stream management and mining algorithms for mining continuously generated data from different components of a vehicle.